Partially Linear Spatial Probit Models
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Partially Linear Spatial Probit Models
Auteur(s) :
AHMED, Mohamed-Salem [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Genin, Michaël [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Hassan, Alaa Ali [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Genin, Michaël [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Hassan, Alaa Ali [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Titre de la revue :
Annales de l'ISUP
Pagination :
71-96
Éditeur :
Publications de l’Institut de Statistique de l’Université de Paris
Date de publication :
2019
ISSN :
1626-1607
Discipline(s) HAL :
Statistiques [stat]/Méthodologie [stat.ME]
Résumé en anglais : [en]
A partially linear probit model for spatially dependent data is considered. A triangular array setting is used to cover various patterns of spatial data. Conditional spatial heteroscedasticity and non-identically distributed ...
Lire la suite >A partially linear probit model for spatially dependent data is considered. A triangular array setting is used to cover various patterns of spatial data. Conditional spatial heteroscedasticity and non-identically distributed observations and a linear process for disturbances are assumed, allowing various spatial dependencies. The estimation procedure is a combination of a weighted likelihood and a generalized method of moments. The procedure first fixes the parametric components of the model and then estimates the non-parametric part using weighted likelihood; the obtained estimate is then used to construct a GMM parametric component estimate. The consistency and asymptotic distribution of the estimators are established under sufficient conditions. Some simulation experiments are provided to investigate the finite sample performance of the estimators.Lire moins >
Lire la suite >A partially linear probit model for spatially dependent data is considered. A triangular array setting is used to cover various patterns of spatial data. Conditional spatial heteroscedasticity and non-identically distributed observations and a linear process for disturbances are assumed, allowing various spatial dependencies. The estimation procedure is a combination of a weighted likelihood and a generalized method of moments. The procedure first fixes the parametric components of the model and then estimates the non-parametric part using weighted likelihood; the obtained estimate is then used to construct a GMM parametric component estimate. The consistency and asymptotic distribution of the estimators are established under sufficient conditions. Some simulation experiments are provided to investigate the finite sample performance of the estimators.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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